212 results about "Demosaicing" patented technology

The invention belongs to the technical field of optical imaging, and particularly relates to a multispectral image demosaicing method based on a galvanometer. The method comprises the steps that S1, a demosaicing device is built, wherein the demosaicing device comprises a lens, a galvanometer module and a spectrum camera which are sequentially arranged in the light path direction; s2, aligning a lens to a to-be-measured target, adjusting a galvanometer module to enable the to-be-measured target to be imaged on the spectrum camera for no less than one time, and obtaining a multispectral mosaic image corresponding to the number of times of imaging; and S3, realizing demosaicing processing on the multispectral mosaic image based on different demosaicing methods according to a quantitative relationship between the imaging times in the step S2 and the number of spectral channels in a single filtering period of the spectral camera. According to the method, the multispectral information of the to-be-measured target is obtained, meanwhile, more spatial position information of the to-be-measured target can be obtained, the sampling rate of the to-be-measured target is increased, loss of pattern texture information is reduced, and rapid and accurate multispectral imaging is achieved.

The invention discloses a combined denoising and demosaicing method for a low-exposure original image under low light, and the method is divided into two stages: in the first stage, introducing a high and low frequency fusion module based on wavelet transform, and generating relatively clean pseudo long exposure data to reduce noise; and in the second stage, through a color space attention subnet, image reconstruction is carried out by using the pseudo long exposure data generated in the first stage, the color is recovered, and texture details are enhanced. In addition, the invention also provides a wavelet loss function to balance detail recovery and noise suppression, and discusses the influence of three different training strategies on the performance of the model. Experimental results show that the method provided by the invention is superior to most advanced algorithms in the aspects of quantitative indexes, color accuracy, detail retention and the like.

The invention relates to the technical field of images, and particularly provides a video-level spectral imaging system and imaging method based on compressed sensing, and the video-level spectral imaging system based on compressed sensing is used for shooting a target to obtain an original multi-spectral image. Each pixel in the original multispectral image comprises spectral information of one wave band, the spectral information corresponds to the spatial position of a spectral channel in the light splitting element, and the original multispectral image is subjected to convolution operation to obtain a preliminary pseudo panchromatic image; performing sharpening operation on each pixel of the preliminary pseudo panchromatic image to obtain a pseudo panchromatic image; obtaining a demosaicing residual error based on the original multispectral image and the pseudo panchromatic image; and finally, restoring the pseudo panchromatic image by using the demosaiced residual error to obtain a demosaiced single-spectrum image of each wave band. The spectrum channels are arranged on the target surface of the light splitting element in a pseudo-random mode, and all spectrum images are accurately and efficiently restored.

The invention discloses an ultra-high-speed imaging device based on acousto-optic filtering modulation. The ultra-high-speed imaging device comprises a spectrum modulation system, a spectrum imaging system and a data processing system. The spectrum modulation system utilizes an acousto-optic tunable filter (AOTF) to dynamically modulate broadband continuous spectrum light, narrow-band illumination light which rapidly changes along with time is generated, and time information of a dynamic scene is effectively mapped to a spectrum dimension. And the spectral imaging system images the spectral coded dynamic scene to the hyperspectral camera to realize acquisition of spectral space-time information. The data processing system performs spectral crosstalk correction, channel separation and demosaicing reconstruction on the acquired original mosaic image, and recovers a time sequence image according to a spectrum-time mapping relation, thereby realizing single-shot superspeed imaging in a dynamic process. The device has the advantages of being simple and compact in structure, flexible in time window, high in imaging fidelity and the like, and has wide application prospects in the fields of rapid dynamics such as microfluidics, laser processing and ultrafast physics.

The invention belongs to the technical field of optical imaging, and particularly relates to a multispectral demosaicing method based on a variable lens. Comprising the steps that S1, a demosaicing device is built, and the demosaicing device comprises a variable lens module and a spectrum camera which are sequentially arranged in the light path direction; s2, after the variable lens module is aligned with the to-be-measured target, the variable lens module is adjusted, so that the to-be-measured target is imaged on the spectrum camera for no less than one time, and a multispectral mosaic image corresponding to the number of times of imaging is obtained; and S3, realizing demosaicing processing on the multispectral mosaic image based on different demosaicing algorithms according to a quantitative relationship between the imaging times in the step S2 and the number of filtering channels in a single filtering period of the spectral camera. According to the method, the sampling rate of real data can be increased, the loss of pattern texture information is reduced, and a high-resolution multispectral image can be directly obtained without using an algorithm under an ideal condition.

The invention discloses a high-robustness demosaicing bilinear interpolation method for polarization hyperspectral imaging, relates to the field of polarization spectral imaging, and solves the problems that in the prior art, when an optical system is located on a platform, vibration occurs, frames of collected images are lost, and spectrum aliasing exists in the optical system. The method comprises the following steps: collecting a group of polarized hyperspectral images, and carrying out high-robustness demosaicing reconstruction on the collected group of polarized hyperspectral images; the image I is recorded as the image of the obtained Kth spectral band, and the mosaic image II is an empty matrix and is used as an interpolation space of the image I; performing assignment on the mosaic image II in the horizontal direction; filling by using boundary conditions to complete interpolation in the horizontal direction; extracting polarization channel intensity images in four different directions in the mosaic image II, and performing vertical direction interpolation on the polarization channel intensity images; and polarization demosaicing reconstruction of the Kth spectral band image is completed. The method is suitable for the field of suppressing image artifacts generated in reconstruction.

The invention provides an array image demosaicing method based on dynamic convolution and adaptive coding, which relates to the technical field of image processing, and comprises the following steps: acquiring single-channel original image data and a corresponding color filtering array arrangement type identifier; converting the arrangement type identifier into a multi-dimensional physical feature vector, and inputting the multi-dimensional physical feature vector into a feature processor of a neural network model to generate a weighting coefficient vector; carrying out weighted combination on the plurality of special arrangement transformation matrixes through a weighting coefficient vector to obtain a transformation component, adding the transformation component and a basic convolution kernel parameter to obtain a dynamic convolution kernel parameter, and carrying out directional modulation on a specific spatial position of the dynamic convolution kernel parameter based on a direction weight component in a multi-dimensional physical feature vector; and performing convolution operation on the original image data by using the dynamic convolution kernel parameters, extracting multi-scale features, reconstructing image features, and outputting multi-channel color image data, so that the method can be adaptive to different color filter array types, and the demosaicing precision and generalization capability are improved.

Apparatuses, systems, and techniques to process image data. In at least one embodiment, a neural network is trained to perform demosaicing of two-dimensional image data obtained from an image sensor.

The invention discloses a high-quality multispectral demosaicing method based on residual error guidance, belongs to the technical field of image restoration, and aims to solve the problems that a large amount of high-frequency information is not effectively processed and RGB image chromaticity channel restoration is insufficient. The method comprises the following steps: step 1, designing nine-channel multispectral optical filter array arrangement; step 2, interpolating a dense sampling channel image; 3, preliminarily reconstructing a sparse sampling channel image; and 4, further restoring the sparse sampling channel image by using a residual interpolation method. According to the demosaicing method based on residual guidance, when pixels of other channels are reconstructed, interpolation is carried out on residual values, and the problems of key detail reservation, fuzziness and detail loss are emphasized. And the mutual influence between different spectrum channels is considered, so that more accurate color restoration is realized, and the visual perception and the practical application value of the image are improved. The reconstructed image is excellent in the aspect of recovering the chromaticity channel of the RGB image, and the real effect of the reconstructed image is closer to that of the original image.

The invention provides a local color difference self-adaptive image demosaicing interpolation method, a system-level chip and a computer storage medium. The method comprises the following steps: acquiring an original RAW image; taking a current target pixel in the RAW image as a reference, and extracting a cache window; in the cache window, gradient values of the current target pixel in the horizontal direction, the vertical direction, the diagonal direction and the reverse diagonal direction are calculated; determining an interpolation direction according to the gradient value; and along the interpolation direction, carrying out RGB full-channel interpolation calculation. In the interpolation process, according to the difference between the center point and the two ends of the main direction, the weights of the pixel points participating in interpolation are obtained in a self-adaptive mode, the interpolation precision can be guaranteed, meanwhile, the size of a calculation window is reduced, the resolution of the image can be effectively improved in the interpolation calculation process, the quality of the demosaiced image is improved, and the image quality is improved. The consumption of hardware resources can be saved, and the problem that the image quality is poor due to the fact that an interpolation method adopted in an existing demosaicing process is poor in precision and high in hardware consumption is solved.

The invention discloses a snapshot multispectral camera demosaicing method and device based on a binary tree coating array, and the method comprises the steps: extracting a plurality of single-spectrum mosaic images according to a multispectral original image and a spectrum, and carrying out the estimation of a pseudo panchromatic estimation image according to the multispectral original image; calculating a difference value between each spectral band sparse pseudo panchromatic estimation image and the original spectral band sparse image, and calculating difference values at all pixel positions; adding the difference image after filtering interpolation with the pseudo panchromatic estimation image to obtain a demosaicing reconstruction result based on the pseudo panchromatic estimation image; and enhancing the spatial high-frequency information of the binary tree low-node spectral band image based on the binary tree high-node spectral band image by using the color and spatial correlation between adjacent spectral bands. According to the method, the problem of artifacts caused by weak spectrum correlation of the wide spectrum distribution optical filter can be solved, the artifacts of the reconstructed image are reduced, and the spatial resolution capability of the demosaiced image is improved.

The invention belongs to the technical field of polarization image processing, and particularly relates to a color polarization image demosaicing image fusion method based on deep learning, which comprises the following steps: S1, constructing a color polarization image demosaicing convolutional neural network model, and carrying out demosaicing processing on color polarization image data with missing pixel values to obtain a color polarization image demosaicing convolutional neural network model; and generating four panchromatic polarization images with different polarization angles, wherein the convolutional neural network model is composed of two sub-networks, namely a color demosaicing network and a polarization demosaicing network. The method can effectively solve the pixel interpolation problem of CPFA data of an existing color polarization image, improves the definition of the color polarization image, makes up for the technical defects of polarization imaging in a dynamic environment, provides the color polarization image based on the attention mechanism, analyzes the difference of imaging target information expressed by a color intensity image and a color polarization degree image, and improves the image quality. And an attention mechanism module is introduced in a targeted manner, so that the obtained fused image has better quality compared with a traditional fused image.

The invention discloses an image processing method and system, a storage medium and an electronic device, which are applied to an image processing system, and the method comprises the steps: obtaining a mosaic color event signal and a blurred image from an image collection module; inputting the mosaic color event signal and the blurred image into an event demosaicing module of the image processing model to obtain a demosaicing full-color event signal output by the event demosaicing module; inputting the mosaic color event signal and the blurred image into an image deblurring module of the image processing model to obtain a deblurred intermediate image output by the image deblurring module; and inputting the demosaiced full-color event signal and the deblurred intermediate image into a fusion module of the image processing model, and obtaining a deblurred image output by the fusion module.

Disclosed is imaging system with image sensor; pose-tracking means; and processor(s) configured to capture a sequence of images to determine corresponding poses of the image sensor with respect to which images are captured, wherein when capturing image, the processor(s) is configured to perform demosaicing on image data; identify static region(s) in the image(s); obtain new image data to determine a new pose of image sensor; reproject image(s) from a corresponding pose of image sensor with respect to which image(s) is captured to new pose of image sensor, wherein static region(s) in the image(s) is reprojected to generate reprojected static region(s); and generate a new image corresponding to the new image data, by performing temporal reconstruction of static region(s) in the new image that corresponds to the static region(s) of the image(s), based on the reprojected static region(s) of the reprojected image(s).

The invention provides an interpolation method and device for a RAW image in a tetrabayer format and electronic equipment. The interpolation method comprises the following steps: acquiring the RAW image in the tetrabayer format; gradient information of the current pixel point is calculated; calculating a weight factor of the current pixel point according to the gradient information; and calculating a G channel value, a G-R color difference value and a G-B color difference value of the current pixel point by using the weight factor so as to obtain an RGB channel value of the current pixel point and output an RGB image. Gradient information of a current pixel point is calculated, and then a weight factor is calculated, so that edge information of the image can be obtained; the RGB channel value of the current pixel point is calculated through the weight factor, an image conversion result with small noise in a flat area and clear details in an edge area can be obtained, an existing demosaicing algorithm is not needed, conversion from the RAW image in the tetrabayer format to the RGB image is achieved, the image quality is guaranteed, and the image conversion efficiency is improved. The problem of how to convert the RAW image in the tetrabayer format into the high-quality color image is solved.

The present application discloses an image reconstruction method, an apparatus, a device, a medium, and a product. According to the image reconstruction method in the embodiments of the present application, all Swin Transformer modules of an encoding module are connected by means of a patch merging layer; all Swin Transformer modules of a decoding module are connected by means of a patch expanding layer; the encoding module and the decoding module are symmetrical in structure; and finally, an upsampled feature map is input into a reconstruction module so as to obtain an RGB image by means of conversion. By constructing a network on the basis of a U-Net structure, the present solution is able to restore a corresponding high-resolution RGB image from a feature map, and interpolate missing values and defective pixels in a RAW image captured by an event camera while performing demosaicing, thereby obtaining a high-quality RGB image. Moreover, the use of Swin Transformer architecture as a feature extractor allows for the effective capturing of long-range dependencies, and ensures that consistency of local and global information is maintained.

The invention relates to the technical field of image processing, solves the technical problem that color correction and white balance correction of images under different light sources are not ideal in the prior art, and particularly relates to a self-adaptive color correction method and system. The method comprises the following steps: obtaining a plurality of calibration images obtained by shooting 24 color cards by a camera under different light sources in a color temperature box, and carrying out color calibration on the calibration images to obtain calibration parameters under different light sources; acquiring an image shot by a camera in any scene, and performing demosaicing processing to generate an image IMG; calculating a white balance parameter of the image IMG; and calculating color correction matrix parameters of the image IMG according to the calibration parameters. According to the method, the parameter calibration efficiency can be improved, the workload is reduced, effective balance correction and color correction in different scenes are realized, and automatic balance correction and color correction are realized, so that the manual operation of a user is reduced, and the use convenience is improved.

Disclosed is an imaging system with image sensor and processor(s). The image sensor has a plurality of photo-sensitive cells and a colour filter array. The processor(s) is configured to read out unprocessed colour data from those photo-sensitive cells that correspond to the colour filters of the at least three different colours; generate coarse depth data by processing data read out from at least one of: those photo-sensitive cells that correspond to the colour filters that allow the at least one infrared wavelength to pass through the PDAF photo-sensitive cells; perform interpolation and demosaicking on the unprocessed colour data and the coarse depth data, using at least one neural network, to generate a full-resolution colour data and a full-resolution depth data; and reproject the full-resolution colour data from a given camera pose to a given eye pose of a user, by utilising the full-resolution depth data.

The invention provides an image processing model training method and device, an image processing method and device, equipment and a medium, and relates to the technical field of image processing, and the training method comprises the steps: obtaining a sample image of a first format and a reference image of a second format; performing position coding on the sample image by adopting a coding network in the image processing model to obtain a coding feature map; adopting a demosaicing network in an image processing model to carry out demosaicing processing on the coded feature map to obtain an output feature map; fusing the coding feature map and the output feature map to obtain a fused feature map; and training an image processing model based on the fused feature map and the reference image. Therefore, position coding is introduced in the modeling stage, the performance and effect of the model can be improved, and the quality of the finally generated image in the second format is improved.

The invention discloses an image subgradient constrained dual-camera snapshot spectral imaging system and an imaging method, and relates to the field of spectral imaging, in particular to an image subgradient constrained dual-camera snapshot spectral imaging method. The method aims at the technical bottleneck that a traditional spectral imaging system cannot be suitable for a dynamic scene and a low-illumination environment due to low time resolution and limited light utilization rate, and the problem that an existing SCSI system is poor in reconstruction accuracy of a decoding algorithm due to the fact that the compression ratio of space-spectral information is too high or information complementarity is insufficient. The method comprises the following steps: 1, obtaining a preprocessed broadband filter array RAW image and an SD-CASSI compressed encoding image Y; 2, obtaining a demosaiced multiband image; 3, obtaining a coarse estimation spectrum image; 4, calculating the subgradient of the three-dimensional total variation function f3DTV (.) at the roughly estimated spectral image, wherein the subgradient represents the spatial characteristics of the spectral image; and 5, iteratively solving a spectral image recovery problem by using an optimization algorithm to reconstruct a spectral image.

Disclosed is a joint denoising and demosaicking method for a color RAW image guided by a monochrome image. The method comprises: constructing a synthetic image dataset of a monochrome-color binocular camera system for training and testing of network modules; constructing an aligned guidance image generation module by utilizing a structural correlation between a monochrome image and a color image, using a clean grayscale image corresponding to the RAW image as supervision, and training the aligned guidance image generation module by using a perceptual loss function to generate a high-quality aligned guidance image; using the generated aligned guidance image to guide the joint denoising and demosaicking process of the color RAW image; and training a guided denoising and demosaicking module ensure the accuracy of the color of the denoising and demosaicking result while accurately transferring the guidance image structure.

An imaging system includes an image sensor, a wobulator employed to perform sub-pixel shifts; and processor(s) configured to: obtain, in a cycle, two or three sub-images from the image sensor; control the wobulator to perform, during said cycle, one or two sub-pixel shifts, wherein step sizes of sub-pixel shifts vary within at least one of: same cycle, different cycles, wherein at least one of step sizes is X pixel, wherein X is a fraction lying between 0 and 1, at least one other of step sizes is Y pixels, wherein Y is an integer lying in a range from 1 to Z, Z being equal to a number of pixels of same colour lying along a direction of sub-pixel shift in a smallest repeating M×N array (204a-b) in the image sensor; and process the two or three sub-images to generate image(s).

The image demosaicing method comprises the following steps: determining a red image pixel value, a green image pixel value and a blue image pixel value for a red pixel of a Bayer unit, such that each of the first weighted sum, the second weighted sum, and the third weighted sum of the red image pixel value, the green image pixel value, and the blue image pixel value is equal to the respective sensor pixel value. The first weighted sum is equal to the red sensor pixel value output by the red pixel. The second weighted sum is equal to an average value of green sensor pixel values output by green pixels of the Bayer unit. The third weighted sum is equal to a blue sensor pixel value output by a blue pixel of the Bayer cell. The method further includes outputting a demosaiced image having an RGB triple at a position in the demosaiced image corresponding to a position of the red pixel, the RGB triple including a red image pixel value, a green image pixel value, and a blue image pixel value.

Processing accuracy of an image analysis process using an AI model is improved by using an image processing system comprising circuitry configured to acquire image data captured by an image sensor, process the image data to generate a non-demosaic image, and selectively output the non-demosaic image to an artificial intelligence model trained to perform image analysis.

This disclosure provides a demosaicing device and a demosaicing method for image sensor. The method includes capturing a panchromatic image by using a pixel array including switchable pixels and clear pixels, where the panchromatic image is captured when the switchable pixels and the clear pixels are under a clear state. The method includes capturing a chromic image by using the pixel array when the switchable pixels are under a color state and the clear pixels are under the clear state. The method includes performing a weighted average calculation and a signal subtraction on the chromic image and the panchromatic image to generate first resolution chromic images. A resolution of the first resolution chromic images is the same as a resolution of the panchromatic image and the chromic image. The method includes fusing the first resolution chromic images with the panchromatic image to generate demosaiced chromic images.

Disclosed are systems and techniques for image demosaicing with minimal artifacts. The techniques include computing a color value within a first color space for a first pixel of a plurality of pixels based at least on color values within the first color space of a first group of neighboring pixels of the first pixel and computing a first chrominance value within a second color space for the first pixel based at least on the computed color value within the first color space. The techniques include computing a luminance value within the second color space for the first pixel based at least on the first chrominance value within the second color space and converting the luminance value within the second color space and the first chrominance value within the second color space to an output pixel value within a third color space.

A method for generating a digital zoom image by using generative AI may comprise the steps of: acquiring a multi-frame input; performing demosaicing and denoising on the multi-frame input, wherein a denoising intensity is adjusted to be weaker as the number of a plurality of frames included in the multi-frame input increases; acquiring a base image by merging the plurality of frames on which the demosaicing and denoising have been performed; and acquiring a zoomed image by performing super-resolution processing on the base image.

The invention belongs to the field of solid surface temperature shape measurement, and provides a three-dimensional shape and surface temperature synchronous measurement method. Comprising the steps of three-color mask three-dimensional imaging system setting, test environment building, spectral response coefficient calibration, three-color image acquisition, color channel separation, demosaicing hole interpolation, color crosstalk correction, optical flow estimation, three-dimensional coordinate acquisition, three-dimensional grid model generation, temperature evaluation and data fusion. According to the method, the multi-view information is simulated by using the single camera through the three-color mask, the equipment cost is reduced, the temperature is evaluated by using the brightness information of different color channels while multi-view three-dimensional reconstruction is performed on the same data through the multi-view information under the three-color mask, and the result of synchronously monitoring the temperature and the shape is achieved.

A medical device that includes a sensor including a filter array. The sensor is configured to capture a raw image and the filter array is configured to filter the raw image into a frame of raw pixel values including first, second, and third pixel values. The medical device includes non-transitory computer readable medium storing demosaicing instructions that, when executed, causes a processor to exclude the first pixel values from the frame of raw pixel values. The processor generates estimated second pixel values at locations of the excluded first pixel values and the third pixel values on the frame, and generates estimated third pixel values at locations of the excluded first pixel values and the second pixel values on the frame. The processor creates a processed image having a partial-resolution frame from the second pixel values, estimated second pixel values, third pixel values, and estimated third pixel values.

The present disclosure provides methods, apparatuses, systems, and computer-readable mediums for demosaicing images by an apparatus. A method includes obtaining, from a plurality of sensors of the apparatus, a plurality of mosaic images, concatenating each image of the plurality of mosaic images with encoded embeddings of the corresponding mosaic pattern of the sensor of the plurality of sensors that captured the image of the plurality of mosaic images, providing the concatenated plurality of mosaic images to a machine learning model, and acquiring, from the machine learning model, a plurality of demosaiced images corresponding to the plurality of mosaic images. Each sensor of the plurality of sensors has a corresponding mosaic pattern from among a plurality of mosaic patterns.